Can socially sustainable development be achieved through homestead withdrawal? A hybrid multiple-attributes decision analysis

Achieving sustainable rural development is a global challenge, particularly concerning social sustainability. China's rapid urbanization has led to the implementation of WRH policies as a core strategy for sustainable rural development. While economic and environmental impacts of WRH have been studied, its effect on social sustainability remains less explored. This study addresses this gap by proposing a comprehensive assessment model considering the complex interdependencies between multiple dimensions of social sustainability. The research questions focus on the effectiveness of WRH in promoting sustainable rural social development, the relevant evaluation criteria, and the key contributing dimensions. The study is framed within the broader context of China's rural revitalization initiatives and the challenges of rural decline, including population loss, social capital erosion, and economic disparities between urban and rural areas. The existing literature lacks evaluation indicators specifically tailored to assessing the social sustainability outcomes of WRH policies. The study highlights the need for a more nuanced approach that accounts for the complex interplay between various social dimensions.

The literature review examines existing tools and metrics for assessing social sustainability in rural communities, noting a bias towards environmental sustainability. Several indices like ISEW, SNBI, and SWI are discussed, but their limitations in comprehensively capturing the quality of social development are highlighted. The review also analyzes existing rural development indices, pointing out the use of both additive and geometric means for aggregating indicators. However, a systematic framework for assessing the social performance of WRH policies is lacking. The study draws on existing literature to identify indicators and dimensions for evaluating WRH, focusing on the socio-ecological environment (improved water and air quality, sanitation, safety, and landscape coherence), social welfare (happiness enhancement, accessibility, public service facilities, social welfare protection, homeownership security, and satisfaction with WRH policies), social equity (support for disadvantaged groups, improved education, stronger livelihoods, reduced income inequality, and guaranteed compensation), and social inclusion (enhanced sense of belonging, social integration, preservation of local heritage and cultural activities, and culturally sensitive design).

The study employs a two-stage approach: First, a modified Delphi method is used to refine the evaluation criteria, achieving consensus among 13 experts. The Delphi process involved two rounds of questionnaires, with feedback provided to the experts between rounds. The questionnaires involved a seven-point Likert scale for rating the importance of indicators. Consensus criteria were established based on standard measures (mean, standard deviation, coefficient of variation). Criteria exhibiting low importance or inconsistency were adjusted. Second, Fuzzy DEMATEL is applied to analyze the complex causal relationships among the dimensions and criteria. This technique incorporates fuzzy set theory to handle uncertainty and subjective judgments. A Fuzzy DEMATEL questionnaire was designed using a 5-point Likert scale to assess the impact of relationships between criteria. The resulting data are analyzed to construct an influence matrix, generating an Influence Network Relations Map (INRM) to visualize the interrelationships between criteria and dimensions. The panel for the Fuzzy DEMATEL stage comprised 15 experts, including additional members from the real estate sector to ensure precise expert opinions on the impact of building construction aspects. Fuzzy linguistic variables were used to convert the qualitative judgments into triangular fuzzy numbers for processing within the Fuzzy DEMATEL model. The crisp values were obtained using the Converting Fuzzy data into Crisp Scores (CFCS) method by Opricovic and Tzeng (2003). Row and column sums are then calculated to assess the causal relationships and identify key influencing factors.

The Delphi method resulted in a refined set of 20 criteria across four dimensions. Fuzzy DEMATEL analysis revealed that social welfare emerged as the most influential dimension, followed by social equity and socio-ecological environment. Social inclusion showed less influence compared to other dimensions. Among the 20 criteria, improving education levels, securing housing compensation benefits, and enhancing village mobility were the most influential, impacting many other criteria. Conversely, criteria such as enhancing happiness and strengthening social integration showed outcome characteristics. The INRM visualizations provided a clear overview of the complex interactions and causal relationships between dimensions and criteria. The findings showed that improving education levels (C₁₃) and securing housing compensation benefits (C₉) were the most influential criteria. The study's results also confirmed that improving residents' living conditions, enhancing their sense of belonging, and promoting social integration are critical factors for achieving success with WRH policies. The less influential factors were promoting social equality in employment, improving the built environment, and enhancing village mobility. The analysis further highlighted the mediating role of social welfare, demonstrating its influence on both social equity and social inclusion.

The findings address the research questions by identifying key criteria and dimensions contributing to socially sustainable rural development through WRH. The study's emphasis on the complex causal relationships between various factors, using Fuzzy DEMATEL, offers a more nuanced understanding than traditional approaches. The results underscore the importance of considering social welfare, social equity, and socio-ecological aspects alongside economic benefits in assessing the success of WRH policies. The identification of education and compensation as highly influential factors supports the need for targeted interventions to support disadvantaged groups and improve the quality of life for rural residents. The significant role of social welfare highlights its importance as an antecedent variable and its direct influence on social equity. The framework used provides a practical tool for policymakers to evaluate the social sustainability of WRH policies and improve future implementations. The findings are relevant for researchers and practitioners working on rural development, land policy, and sustainable development.

This study provides a novel framework for evaluating the social sustainability outcomes of WRH policies in China. The hybrid model, combining Delphi and Fuzzy DEMATEL, offers a robust and nuanced approach. The key findings highlight the critical importance of focusing on improving education, securing compensation benefits, and enhancing social welfare to achieve socially sustainable rural development. Future research could explore the applicability of this model in diverse contexts and integrate it with other decision-making methods like ANP for more comprehensive evaluation. Further investigation could also focus on regional variations in the impact of WRH and the long-term effects of these policies on social cohesion and economic opportunities. The development of a more refined assessment system considering qualitative factors, such as public perceptions of social equity and inclusion, will aid future policy design and implementation.

The study's findings are based on expert opinions and may not fully reflect the diverse experiences and perspectives of all rural residents. The sample of experts, while carefully selected, might not fully capture the geographical diversity across China. Future research should include a broader range of stakeholders, including rural residents themselves, to gain a more comprehensive understanding of the social impacts of WRH policies. The selection of indicators could benefit from further validation through field studies and additional data collection methodologies.